mike@bucasb.bu.edu (Michael Cohen) (12/17/89)
BOSTON UNIVERSITY, A WORLD LEADER IN NEURAL NETWORK RESEARCH AND TECHNOLOGY,
PRESENTS TWO MAJOR SCIENTIFIC EVENTS:
MAY 6--11, 1990
NEURAL NETWORKS: FROM FOUNDATIONS TO APPLICATIONS
A self-contained systematic course by leading neural architects.
MAY 11--13, 1990
NEURAL NETWORKS FOR AUTOMATIC TARGET RECOGNITION
An international research conference presenting INVITED and CONTRIBUTED
papers, herewith solicited, on one of the most active research topics in
science and technology today.
SPONSORED BY
THE CENTER FOR ADAPTIVE SYSTEMS,
THE GRADUATE PROGRAM IN COGNITIVE AND NEURAL SYSTEMS,
AND
THE WANG INSTITUTE
OF
BOSTON UNIVERSITY
WITH PARTIAL SUPPORT FROM
THE AIR FORCE OFFICE OF SCIENTIFIC RESEARCH
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CALL FOR PAPERS
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NEURAL NETWORKS FOR AUTOMATIC TARGET RECOGNITION
MAY 11--13, 1990
This research conference at the cutting edge of neural network science and
technology will bring together leading experts in academe, government, and
industry to present their latest results on automatic target recognition in
invited lectures and contributed posters. Automatic target recognition is a
key process in systems designed for vision and image processing, speech and
time series prediction, adaptive pattern recognition, and adaptive
sensory-motor control and robotics. It is one of the areas emphasized by the
DARPA Neural Networks Program, and has attracted intense research activity
around the world. Invited lecturers include:
JOE BROWN, Martin Marietta, "Multi-Sensor ATR using Neural Nets"
GAIL CARPENTER, Boston University, "Target Recognition by Adaptive Resonance:
ART for ATR"
NABIL FARHAT, University of Pennsylvania, "Bifurcating Networks for Target
Recognition"
STEPHEN GROSSBERG, Boston University, "Recent Results on Self-Organizing
ATR Networks"
ROBERT HECHT-NIELSEN, HNC, "Spatiotemporal Attention Focusing by Expectation
Feedback"
KEN JOHNSON, Hughes Aircraft, "The Application of Neural Networks to the
Acquisition and Tracking of Maneuvering Tactical Targets in High Clutter
IR Imagery"
PAUL KOLODZY, MIT Lincoln Laboratory, "A Multi-Dimensional ATR System"
MICHAEL KUPERSTEIN, Neurogen, "Adaptive Sensory-Motor Coordination using
the INFANT Controller"
YANN LECUN, AT&T Bell Labs, "Structured Back Propagation Networks for
Handwriting Recognition"
CHRISTOPHER SCOFIELD, Nestor, "Neural Network Automatic Target Recognition
by Active and Passive Sonar Signals"
STEVEN SIMMES, Science Applications International Co., "Massively Parallel
Approaches to Automatic Target Recognition"
ALEX WAIBEL, Carnegie Mellon University, "Patterns, Sequences and Variability:
Advances in Connectionist Speech Recognition"
ALLEN WAXMAN, MIT Lincoln Laboratory, "Invariant Learning and Recognition of
3D Objects from Temporal View Sequences"
FRED WEINGARD, Booz-Allen and Hamilton, "Current Status and Results of Two
Major Government Programs in Neural Network-Based ATR"
BARBARA YOON, DARPA, "DARPA Artificial Neural Networks Technology Program:
Automatic Target Recognition"
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CALL FOR PAPERS---ATR POSTER SESSION: A featured poster session on ATR
neural network research will be held on May 12, 1990. Attendees who wish to
present a poster should submit 3 copies of an extended abstract
(1 single-spaced page), postmarked by March 1, 1990, for refereeing. Include
with the abstract the name, address, and telephone number of the corresponding
author. Mail to: ATR Poster Session, Neural Networks Conference, Wang
Institute of Boston University, 72 Tyng Road, Tyngsboro, MA 01879. Authors
will be informed of abstract acceptance by March 31, 1990.
SITE: The Wang Institute possesses excellent conference facilities on a
beautiful 220-acre rustic setting. It is easily reached from Boston's Logan
Airport and Route 128.
REGISTRATION FEE: Regular attendee--$90; full-time student--$70. Registration
fee includes admission to all lectures and poster session, one reception, two
continental breakfasts, one lunch, one dinner, daily morning and afternoon
coffee service. STUDENTS: Read below about FELLOWSHIP support.
REGISTRATION: To register by telephone with VISA or MasterCard call
(508) 649-9731 between 9:00AM--5:00PM (EST). To register by FAX, fill out
the registration form and FAX back to (508) 649-6926. To register by mail,
complete the registration form and mail with your full form of payment as
directed. Make check payable in U.S. dollars to "Boston University". See below
for Registration Form. To register by electronic mail, use the address
"rosenber@bu-tyng.bu.edu". On-site registration on a space-available basis will
take place from 1:00--5:00PM on Friday, May 11. A RECEPTION will be held from
3:00--5:00PM on Friday, May 11. LECTURES begin at 5:00PM on Friday, May 11
and conclude at 1:00PM on Sunday, May 13.
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NEURAL NETWORKS: FROM FOUNDATIONS TO APPLICATIONS
MAY 6--11, 1989
This in-depth, systematic, 5-day course is based upon the world's leading
graduate curriculum in the technology, computation, mathematics, and biology
of neural networks. Developed at the Center for Adaptive Systems (CAS) and
the Graduate Program in Cognitive and Neural Systems (CNS) of Boston
University, twenty-eight hours of the course will be taught by six CAS/CNS
faculty. Three distinguished guest lecturers will present eight hours of
the course.
COURSE OUTLINE
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MAY 7, 1990
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MORNING SESSION (PROFESSOR GROSSBERG)
HISTORICAL OVERVIEW:
Introduction to the binary, linear, and continuous-nonlinear streams of
neural network research: McCulloch-Pitts, Rosenblatt, von Neumann; Anderson,
Kohonen, Widrow; Hodgkin-Huxley, Hartline-Ratliff, Grossberg.
CONTENT ADDRESSABLE MEMORY:
Classification and analysis of neural network models for absolutely stable CAM.
Models include: Cohen-Grossberg, additive, shunting, Brain-State-In-A-Box,
Hopfield, Boltzmann Machine, McCulloch-Pitts, masking field, bidirectional
associative memory.
COMPETITIVE DECISION MAKING:
Analysis of asynchronous variable-load parallel processing by shunting
competitive networks; solution of noise-saturation dilemma; classification of
feedforward networks: automatic gain control, ratio processing, Weber law, total
activity normalization, noise suppression, pattern matching, edge detection,
brightness constancy and contrast, automatic compensation for variable
illumination or other background energy distortions; classification of feedback
networks: influence of nonlinear feedback signals, notably sigmoid signals,
on pattern transformation and memory storage, winner-take-all choices, partial
memory compression, tunable filtering, quantization and normalization of total
activity, emergent boundary segmentation; method of jumps for classifying
globally consistent and inconsistent competitive decision schemes.
ASSOCIATIVE LEARNING:
Derivation of associative equations for short-term memory and long-term memory.
Overview and analysis of associative outstars, instars, computational maps,
avalanches, counterpropagation nets, adaptive bidrectional associative memories.
Analysis of unbiased associative pattern learning by asynchronous parallel
sampling channels; classification of associative learning laws.
AFTERNOON SESSION (PROFESSORS JORDAN AND MINGOLLA)
COMBINATORIAL OPTIMIZATION
PERCEPTRONS:
Adeline, Madeline, delta rule, gradient descent, adaptive statistical predictor,
nonlinear separability.
INTRODUCTION TO BACK PROPAGATION:
Supervised learning of multidimensional nonlinear maps, NETtalk, image
compression, robotic control.
RECENT DEVELOPMENTS OF BACK PROPAGATION:
This two-hour guest tutorial lecture will provide a systematic review of recent
developments of the back propagation learning network, especially focussing on
recurrent back propagation variations and applications to outstanding
technological problems.
EVENING SESSION: DISCUSSIONS WITH TUTORS
MAY 8, 1990
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MORNING SESSION (PROFESSORS CARPENTER AND GROSSBERG)
ADAPTIVE PATTERN RECOGNITION:
Adaptive filtering; contrast enhancement; competitive learning of recognition
categories; adaptive vector quantization; self-organizing computational maps;
statistical properties of adaptive weights; learning stability and causes of
instability.
INTRODUCTION TO ADAPTIVE RESONANCE THEORY:
Absolutely stable recognition learning, role of learned top-down expectations;
attentional priming; matching by 2/3 Rule; adaptive search; self-controlled
hypothesis testing; direct access to globally optimal recognition code; control
of categorical coarseness by attentional vigilance; comparison with relevant
behavioral and brain data to emphasize biological basis of ART computations.
ANALYSIS OF ART 1:
Computational analysis of ART 1 architecture for self-organized real-time
hypothesis testing, learning, and recognition of arbitrary sequences of
binary input patterns.
AFTERNOON SESSION (PROFESSOR CARPENTER)
ANALYSIS OF ART 2:
Computational analysis of ART 2 architecture for self-organized real-time
hypothesis testing, learning, and recognition for arbitrary sequences of analog
or binary input patterns.
ANALYSIS OF ART 3:
Computational analysis of ART 3 architecture for self-organized real-time
hypothesis testing, learning, and recognition within distributed network
hierarchies; role of chemical transmitter dynamics in forming a memory
representation distinct from short-term memory and long-term memory;
relationships to brain data concerning neuromodulators and synergetic ionic and
transmitter interactions.
SELF-ORGANIZATION OF INVARIANT PATTERN RECOGNITION CODES:
Computational analysis of self-organizing ART architectures for recognizing
noisy imagery undergoing changes in position, rotation, and size.
NEOCOGNITION:
Recognition and completion of images by hierarchical bottom-up filtering and
top-down attentive feedback.
EVENING SESSION: DISCUSSIONS WITH TUTORS
MAY 9, 1990
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MORNING SESSION (PROFESSORS GROSSBERG & MINGOLLA)
VISION AND IMAGE PROCESSING:
Introduction to Boundary Contour System for emergent segmentation and Feature
Contour System for filling-in after compensation for variable illumination;
image compression, orthogonalization, and reconstruction; multidimensional
filtering, multiplexing, and fusion; coherent boundary detection,
regularization, self-scaling, and completion; compensation for variable
illumination sources, including artificial sensors (infrared sensors,
laser radars); filling-in of surface color and form; 3-D form from
shading, texture, stereo, and motion; parallel processing of static form and
moving form; motion capture and induced motion; synthesis of static form and
motion form representations.
AFTERNOON SESSION (PROFESSORS BULLOCK, COHEN, & GROSSBERG)
ADAPTIVE SENSORY-MOTOR CONTROL AND ROBOTICS:
Overview of recent progress in adaptive sensory-motor control and related
robotics research. Reaching to, grasping, and transporting objects of variable
mass and form under visual guidance in a cluttered environment will be used as a
target behavioral competence to clarify subproblems of real-time adaptive
sensory-motor control. The balance of the tutorial will be spent detailing
neural network modules that solve various subproblems. Topics include:
Self-organizing networks for real-time control of eye movements, arm movements,
and eye-arm coordination; learning of invariant body-centered target position
maps; learning of intermodal associative maps; real-time trajectory formation;
adaptive vector encoders; circular reactions between action and sensory
feedback; adaptive control of variable speed movements; varieties of error
signals; supportive behavioral and neural data; inverse kinematics; automatic
compensation for unexpected perturbations; independent adaptive control of
force and position; adaptive gain control by cerebellar learning;
position-dependent sampling from spatial maps; predictive motor planning
and execution.
SPEECH PERCEPTION AND PRODUCTION:
Hidden Markov models; self-organization of speech perception and production
codes; eighth nerve Average Localized Synchrony Response; phoneme recognition by
back propagation, time delay networks, and vector quantization.
MAY 10, 1990
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MORNING SESSION (PROFESSORS COHEN, GROSSBERG, & MERRILL)
SPEECH PERCEPTION AND PRODUCTION:
Disambiguation of coarticulated vowels and consonants; dynamics of working
memory; multiple-scale adaptive coding by masking fields; categorical
perception; phonemic restoration; contextual disambiguation of speech tokens;
resonant completion and grouping of noisy variable-rate speech streams.
REINFORCEMENT LEARNING AND PREDICTION:
Recognition learning, reinforcement learning, and recall learning are the 3 R's
of neural network learning. Reinforcement learning clarifies how external
events interact with internal organismic requirements to trigger learning
processes capable of focussing attention upon and generating appropriate actions
towards motivationally desired goals. A neural network model will be derived to
show how reinforcement learning and recall learning can self-organize in
response to asynchronous series of significant and irrelevant events. These
mechanisms also control selective forgetting of memories that are no longer
predictive, adaptive timing of behavioral responses, and self-organization of
goal directed problem solvers.
AFTERNOON SESSION
(PROFESSORS GROSSBERG & MERRILL AND DR. HECHT-NIELSEN)
REINFORCEMENT LEARNING AND PREDICTION:
Analysis of drive representations, adaptive critics, conditioned reinforcers,
role of motivational feedback in focusing attention on predictive data;
attentional blocking and unblocking; adaptively timed problem solving; synthesis
of perception, recognition, reinforcement, recall, and robotics mechanisms into
a total neural architecture; relationship to data about hypothalamus,
hippocampus, neocortex, and related brain regions.
RECENT DEVELOPMENTS IN THE NEUROCOMPUTER INDUSTRY:
This two-hour guest tutorial will provide an overview of the growth and
prospects of the burgeoning neurocomputer industry by one of its most
important leaders.
EVENING SESSION: DISCUSSIONS WITH TUTORS
MAY 11, 1990
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MORNING SESSION (DR. FAGGIN)
VLSI IMPLEMENTATION OF NEURAL NETWORKS:
This is a four-hour self-contained tutorial on the application and development
of VLSI techniques for creating compact real-time chips embodying neural
network designs for applications in technology. Review of neural networks
from a hardware implementation perspective; hardware requirements and
alternatives; dedicated digital implementation of neural networks;
neuromorphic design methodology using VLSI CMOS technology; applications and
performance of neuromorphic implementations; comparison of
neuromorphic and digital hardware; future prospectus.
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COURSE FACULTY FROM BOSTON UNIVERSITY
-------------------------------------
STEPHEN GROSSBERG, Wang Professor of CNS, as well as Professor of Mathematics,
Psychology, and Biomedical Engineering, is one of the world's leading neural
network pioneers and most versatile neural architects; Founder and 1988
President of the International Neural Network Society (INNS); Founder
and Co-Editor-in-Chief of the INNS journal "Neural Networks"; an editor of
the journals "Neural Computation", "Cognitive Science", and "IEEE Expert";
Founder and Director of the Center for Adaptive Systems; General
Chairman of the 1987 IEEE First International Conference on Neural Networks
(ICNN); Chief Scientist of Hecht-Nielsen Neurocomputer Company (HNC); and one
of the four technical consultants to the national DARPA Neural Network Study.
He is author of 200 articles and books about neural networks,
including "Neural Networks and Natural Intelligence" (MIT Press, 1988),
"Neural Dynamics of Adaptive Sensory-Motor Control" (with Michael Kuperstein,
Pergamon Press, 1989), "The Adaptive Brain, Volumes I and II"
(Elsevier/North-Holland, 1987), "Studies of Mind and Brain" (Reidel Press,
1982), and the forthcoming "Pattern Recognition by Self-Organizing Neural
Networks" (with Gail Carpenter).
GAIL CARPENTER is Professor of Mathematics and CNS; Co-Director of the
CNS Graduate Program; 1989 Vice President of the International Neural Network
Society (INNS); Organization Chairman of the 1988 INNS annual meeting;
Session Chairman at the 1989 and 1990 IEEE/INNS International Joint Conference
on Neural Networks (IJCNN); one of four technical consultants to the national
DARPA Neural Network Study; editor of the journals "Neural Networks",
"Neural Computation", and "Neural Network Review"; and a member of the
scientific advisory board of HNC. A leading neural architect, Carpenter is
especially well-known for her seminal work on developing the adaptive
resonance theory architectures (ART 1, ART 2, ART 3) for adaptive pattern
recognition.
MICHAEL COHEN, Associate Professor of Computer Science and CNS, is a
leading architect of neural networks for content addressable memory
(Cohen-Grossberg model), vision (Feature Contour System), and speech (Masking
Fields); editor of "Neural Networks"; Session Chairman at the 1987 ICNN,
and the 1989 IJCNN; and member of the DARPA Neural Network Study panel on
Simulation/Emulation Tools and Techniques.
ENNIO MINGOLLA, Assistant Professor of Psychology and CNS, is holder of
one of the first patented neural network architectures for vision and image
processing (Boundary Contour System); Co-Organizer of the 3rd Workshop on
Human and Machine Vision in 1985; editor of the journals "Neural Networks"
and "Ecological Psychology"; member of the DARPA Neural Network Study
panel of Adaptive Knowledge Processing; consultant to E.I. duPont de Nemours,
Inc.; Session Chairman for vision and image processing at the 1987 ICNN,
and the 1988 INNS meetings.
DANIEL BULLOCK, Assistant Professor of Psychology and CNS, is developer
of neural network models for real-time adaptive sensory-motor control of arm
movements and eye-arm coordination, notably the VITE and FLETE models for
adaptive control of multi-joint trajectories; editor of "Neural Networks";
Session Chairman for adaptive sensory-motor control and robotics at the 1987
ICNN and the 1988 INNS meetings; invited speaker at the 1990 IJCNN.
JOHN MERRILL, Assistant Professor of Mathematics and CNS, is developing
neural network models for adaptive pattern recognition, speech recognition,
reinforcement learning, and adaptive timing in problem solving behavior, after
having received his Ph.D. in mathematics from the University of Wisconsin at
Madison, and completing postdoctoral research in computer science and
linguistics at Indiana University.
GUEST LECTURERS
---------------
FEDERICO FAGGIN is co-founder and president of Synaptics, Inc. Dr. Faggin
developed the Silicon Gate Technology at Fairchild Semiconductor. He also
designed the first commercial circuit using Silicon Gate Technology: the 3708,
an 8-bit analog multiplexer. At Intel Corporation he was responsible for
designing what was to become the first microprocessor---the 4000 family,
also called MCS-4. He and Hal Feeney designed the 8008, the first 8-bit
microprocessor introduced in 1972, and later Faggin conceived the 8080 and
with M. Shima designed it. The 8080 was the first high-performance 8-bit
microprocessor. At Zilog Inc., Faggin conceived the Z80 microprocessor
family and directed the design of the Z80 CPU. Faggin also started Cygnet
Technologies, which developed a voice and data communication
peripheral for the personal computer. In 1986 Faggin co-founded Synaptics
Inc., a company dedicated to the creation of a new type of VLSI hardware for
artificial neural networks and other machine intelligence applications.
Faggin is the recipient of the 1988 Marconi Fellowship Award for his
contributions to the birth of the microprocessor.
ROBERT HECHT-NIELSEN is co-founder and chairman of the Board of Directors
of Hecht-Nielsen Neurocomputer Corporation (HNC), a
pioneer in neurocomputer technology and the application of neural networks,
and a recognized leader in the field. Prior to the formation of HNC, he
founded and managed the neurocomputer development and neural network
applications at TRW (1983--1986) and Motorola (1979--1983). He has been active
in neural network technology and neurocomputers since 1961 and earned his
Ph.D. in mathematics in 1974. He is currently a visiting lecturer in
the Electrical Engineering Department at the University of California at San
Diego, and is the author of influential technical reports and papers on
neurocomputers, neural networks, pattern recognition, signal processing
algorithms, and artificial intelligence.
MICHAEL JORDAN is an Assistant Professor of Brain and Cognitive Sciences at MIT.
One of the key developers of the recurrent back propagation algorithms,
Professor Jordan's research is concerned with learning in recurrent networks
and with the use of networks as forward models in planning and control. His
interest in interdisciplinary research on neural networks is founded in his
training for a Bachelors degree in Psychology, a Masters degree in Mathematics,
and a Ph.D. in Cognitive Science from the University of California at San
Diego. He was a postdoctoral researcher in Computer Science at the University of
Massachusetts at Amherst before assuming his present position at MIT.
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REGISTRATION FEE: Regular attendee--$950; full-time student--$250.
Registration fee includes five days of tutorials, course notebooks, one
reception, five continental breakfasts, five lunches, four dinners, daily
morning and afternoon coffee service, evening discussion sessions with
leading neural architects.
REGISTRATION: To register by telephone with VISA or MasterCard call
(508) 649-9731 between 9:00AM--5:00PM (EST). To register by FAX, fill out
the registration form and FAX back to (508) 649-6926. To register by mail,
complete the registration form and mail with you full form of payment as
directed. Make check payable in U.S. dollars to "Boston University". See below
for Registration Form. To register by electronic mail, use the address
"rosenber@bu-tyng.bu.edu". On-site registration on a space-available basis will
take place from 2:00--7:00PM on Sunday, May 6 and from 7:00--8:00AM on Monday,
May 7, 1990. A RECEPTION will be held from 4:00--7:00PM on Sunday, May 6.
LECTURES begin at 8:00AM on Monday, May 7 and conclude at 12:30PM on Friday,
May 11.
STUDENT FELLOWSHIPS supporting travel, registration, and lodging for the
Course and the Research Conference are available to full-time graduate
students in a PhD program. Applications must be postmarked by March 1, 1990.
Send curriculum vitae, a one-page essay describing your interest in neural
networks, and a letter from a faculty advisor to: Student Fellowships, Neural
Networks Course, Wang Institute of Boston University, 72 Tyng Road,
Tyngsboro, MA 01879.
CNS FELLOWSHIP FUND: Net revenues from the course will endow fellowships
for Ph.D. candidates in the CNS Graduate Program. Corporate and individual gifts
to endow CNS Fellowships are also welcome. Please write: Cognitive and Neural
Systems Fellowship Fund, Center for Adaptive Systems, Boston University, 111
Cummington Street, Boston, MA 02215.
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REGISTRATION FOR COURSE AND RESEARCH CONFERENCE
Course: Neural Network Foundations and Applications, May 6--11, 1990
Research Conference: Neural Networks for Automatic Target Recognition,
May 11--13, 1990
NAME: _________________________________________________________________
ORGANIZATION (for badge): _____________________________________________
MAILING ADDRESS: ______________________________________________________
______________________________________________________
CITY/STATE/COUNTRY: ___________________________________________________
POSTAL/ZIP CODE: ______________________________________________________
TELEPHONE(S): _________________________________________________________
COURSE RESEARCH CONFERENCE
------ -------------------
[ ] regular attendee $950 [ ] regular attendee $90
[ ] full-time student $250 [ ] full-time student $70
(limited number of spaces) (limited number of spaces)
[ ] Gift to CNS Fellowship Fund
TOTAL PAYMENT: $________
FORM OF PAYMENT:
[ ] check or money order (payable in U.S. dollars to Boston University)
[ ] VISA [ ] MasterCard
Card Number: ______________________________________________
Expiration Date: ______________________________________________
Signature: ______________________________________________
Please complete and mail to:
Neural Networks
Wang Institute of Boston University
72 Tyng Road
Tyngsboro, MA 01879 USA
To register by telephone, call: (508) 649-9731.
HOTEL RESERVATIONS:
Room blocks have been reserved at 3 hotels near the Wang Institute. Hotel
names, rates, and telephone numbers are listed below. A shuttle bus will take
attendees to and from the hotels for the Course and Research Conference.
Attendees should make their own reservations by calling the hotel. The special
conference rate applies only if you mention the name and dates of the meeting
when making the reservations.
Sheraton Tara Red Roof Inn Stonehedge Inn
Nashua, NH Nashua, NH Tyngsboro, MA
(603) 888-9970 (603) 888-1893 (508) 649-4342
$70/night+tax $39.95/night+tax $89/night+tax
The hotels in Nashua are located approximately 5 miles from the Wang
Institute. A shuttle bus will be provided.
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